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arXiv · 2607.16123

BayesContact: Uncertain Pose Estimation via Visuo-Tactile Proposals and Simulation-based Inference

Aditya Kamireddypalli·Matias Mattamala·Joao Moura·Russell Buchanan·Sethu Vijayakumar·Subramanian Ramamoorthy
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Abstract

Contact-rich manipulation requires pose estimates that are often more accurate than what depth-only sensing provides. Existing methods, relying on vision and contact, employ costly offline training procedures that need to be retrained for new environments and geometries. We propose BayesContact, a Simulation-Based Inference framework for visuo-tactile pose estimation in peg-in-hole insertion. BayesContact maintains a particle belief over object pose and fuses depth observations with force/torque-derived contact evidence. We employ simulation based forward models to approximate these observation likelihoods. For each pose hypothesis, a renderer predicts depth measurements and a physics simulator predicts contact outcomes under guarded probing actions; both are scored against real observations to update the belief. The resulting multimodal belief also enables information-gain-based probing for active disambiguation. Across simulated geometries and real-robot experiments, BayesContact improves pose observability and insertion success over vision-only inference by 30%

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Aditya Kamireddypalli, Matias Mattamala, Joao Moura, Russell Buchanan, Sethu Vijayakumar, Subramanian Ramamoorthy. 2026-07-17. BayesContact: Uncertain Pose Estimation via Visuo-Tactile Proposals and Simulation-based Inference. https://arxiv.org/abs/2607.16123

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